PRIVACY-PRESERVING TECHNIQUES IN MACHINE LEARNING MODELS

Abstract

The rapid adoption of machine learning (ML) across diverse sectors has escalated concerns over data privacy, especially when models are trained on sensitive or personal information. This article surveys state-of-the-art privacy-preserving techniques employed in ML models to mitigate privacy risks without significantly compromising model performance. Techniques such as differential privacy, federated learning, homomorphic encryption, secure multiparty computation, and adversarial training are critically examined. Emphasis is placed on their applicability, challenges, and efficiency within real-world scenarios, particularly in contexts involving sensitive data like healthcare, finance, and governmental systems. Empirical analyses and comparative studies highlight trade-offs between privacy guarantees and computational overheads. This paper also explores ongoing research trends and proposes recommendations to improve privacy assurance in ML deployments, with a focus on the Pakistani context.